Abstract:
This thesis analyzes the relationship between air quality and respiratory health that develops a predictive framework to inform public health planning. Measurements of key pollutants (PM2.5, PM10, NO2, SO2, O3) and meteorological variables (temperature, humidity, wind) were combined with respiratory health outcomes to quantify associations and forecast short‐term risk. After data preparation and normalization, an artificial neural network (ANN) was trained for regression (estimating health burden) and classification (assigning risk categories) and evaluated using standard metrics. The analysis indicates that elevated concentrations of fine particulates and nitrogen dioxide are consistently associated with increased respiratory morbidity, while meteorological conditions modify exposure– response patterns and improve predictive performance. The resulting model demonstrates practical utility for early warnings, clinical preparedness, and targeted mitigation in high‐risk locations. Overall, integrating environmental monitoring with machine learning can shift practice from reactive management to proactive prevention. Future work should evaluate generalizability across regions, incorporate additional health endpoints and socioeconomic context, and assess operational deployment and cost‐effectiveness under changing climate conditions.
